





import qiskit





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						qiskit.result.counts.Counts:







  """simple docstring"""





  lowerCamelCase__: str         =qiskit.Aer.get_backend("aer_simulator"						)

  # Create a Quantum Circuit acting on the q register
  lowerCamelCase__: str         =qiskit.QuantumCircuit(__a      ,       __a						)

  # Map the quantum measurement to the classical bits
  circuit.measure([0]      ,       [0]						)

  # Execute the circuit on the simulator
  lowerCamelCase__: Tuple         =qiskit.execute(__a      ,       __a      ,       shots=1000						)

  # Return the histogram data of the results of the experiment.
  return job.result().get_counts(__a						)


if __name__ == "__main__":
     print(f'Total count for various states are: {single_qubit_measure(1, 1)}')



from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Tuple	,  a						:float		)						->					float:
								return 0.0




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     np.ndarray		,    _lowerCamelCase					:     int)   ->       tuple[int | float, int | float]:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   min([-20, np.min(fft_results[1 : samplerate // 2 - 1])])
							__UpperCamelCase      :  Any       =   max([20, np.max(fft_results[1 : samplerate // 2 - 1])])
							return lowest, highest




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   512
							__UpperCamelCase      :  List[Any]       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  List[Any]       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Optional[Any]       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.abs(np.fft.fft(_lowerCamelCase))
							__UpperCamelCase      :  Optional[int]       =   20 * np.logaa(_lowerCamelCase)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							# Display within reasonable bounds
							__UpperCamelCase      :  Optional[Any]       =   get_bounds(_lowerCamelCase		,    _lowerCamelCase)
							plt.ylim(max([-80, bounds[0]])		,    min([80, bounds[1]]))
							plt.ylabel("Gain (dB)")

							plt.plot(_lowerCamelCase)
							plt.show()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   512
							__UpperCamelCase      :  Dict       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  Tuple       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Dict       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.angle(np.fft.fft(_lowerCamelCase))

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							plt.ylim(-2 * pi		,    2 * pi)
							plt.ylabel("Phase shift (Radians)")
							plt.plot(np.unwrap(_lowerCamelCase		,    -2 * pi))
							plt.show()